Calculates a Pearson correlation coefficient and the pvalue for testing noncorrelation.
The Pearson correlation coefficient measures the linear relationship between two datasets. Strictly speaking, Pearson’s correlation requires that each dataset be normally distributed, and not necessarily zeromean. Like other correlation coefficients, this one varies between 1 and +1 with 0 implying no correlation. Correlations of 1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The pvalue roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. The pvalues are not entirely reliable but are probably reasonable for datasets larger than 500 or so.
Parameters: 


Returns:  Pearson’s correlation coefficient and 2tailed pvalue. 
Examples:
from mipylib.numeric import stats
y = [29.81,30.04,41.7,43.71,28.75,37.73,52.25,32.41,25.67,28.17,25.71,36.05,37.62,34.28,38.82,40.15,35.69,28.36,39.56,52.56,54.14,50.76,39.35,43.16]
x = [51.6,46,64.3,83.4,65.9,49.5,88.6,101.4,55.9,41.8,33.4,57.3,66.5,40.5,72.3,70,83.3,65.8,63.1,83.4,102,94,77,77]
r, p = stats.pearsonr(x, y)
print r, p
Result:
>>> run script...
0.700798023949 0.000136713449709